GoBoiler International Internship Program Projects

Cities are ecosystems of socio-economic entities which provide concentrated living, working, education, and entertainment options to its inhabitants. Hundreds of years ago, the significantly smaller population and the abundance of natural resources made city design, and even the functioning of cities in relation to their hinterland, quite straightforward. Unfortunately, that is not the case today with over 3.5 billion people in cities. Rather, cities, and urban spaces of all sizes, are extremely complex and their modeling is far from being solved. In this project, we aim to pull together CS, engineering, agricultural-economics, and social science to collectively exploit our unique opportunity to address this emerging problem. Research activities will span many fields and will perform cross-disciplinary research focused on the idea of designing and simulating the functioning of existing and future cities. Our desire is also to pool this knowledge, identify our unique strengths, and pursue large and ambitious computing projects.

Appearance editing offers a unique way to view visually altered objects with various appearances or visualizations. By carefully controlling how an object is illuminated using digital projectors, we obtain stereoscopic imagery for any number of observers with everything visible to the naked eye (i.e., no need for head-mounts or goggles). Such an ability is useful for various applications, including scientific visualization, virtual restoration of cultural heritage, and display systems. Our previous work has focused on virtual restoration of cultural heritage artifacts, on compensation compliancy, and on improving resolution and overall quality of appearance editing. Going forward we are looking to design and integrate mobile robots that carry the projectors and self-locate and self-organize so as to change the appearance of the target object. We have prototype robots already created using in-house 3D printing technology. Next, we need to improve control logic and various 3D reconstruction algorithms.

The Internet of Things (IoT) refers to the networked interconnection of devices, such as appliances, lighting systems, audio-video entertainment systems, andvehicles. The motivation is to provide a way for users to interrogate and control such systems from any location (e.g., an owner can use a cell phone to control the lights in their house). Many IoT devices will use low-power wireless network connections, such as the IEEE 802.15.4 standard. The Wi-SUN alliance is developing a new set of protocols for use with IoT devices, and our project is building a full reference implementation of the protocols on the Xinu operating system. We are designing a system that is powerful as well as small, elegant, and power-aware. In addition to assessing the protocols, we are looking for ways to make the protocol stack robust and adaptable to electrical noise and other radio interference. One subproject is investigating a novel testbed design that allows us to assess wireless protocols in a way that makes all measurements reproducible, independent of radio-frequency background noise.

Data centers are used to provide public and private cloud services. Companies like Amazon, Microsoft, Google, and Facebook's all have data centers. Computers in a data centers are called "servers", and a Data Center network interconnects all servers with both the data center storage facility and the global Internet. Networking is complicated because Data Centers employ virtualization software, such as VMWare and Virtual Box, that allows virtual machines or containers to be moved from one server to another (to balance the computational load across servers). Moving a VM causes a problem for Internet protocols because the protocols assume an IP address is assigned to a device that does not move. Our project is exploring new network addressing and routing mechanism that will allow a VM to retain its IP address when it moves without incurring extra overhead. The scheme changes the interpretation of addresses inside the Data Center, but still provides end-to-end addressing when a host outside the Data Center communicates with a server inside the Data Center.

Many embedded systems are built around a System on Chip (SoC) -- a single VLSI chip that contains a processor, memory, and I/O interfaces. We are working with software for SoC systems. At present, we are using the Galileo board that employs Intel's Quark processor to provide an x86 architecture and BeagleBone boards (Black and Green) that each employ an SoC from TI to provide an ARM architecture. We have ported the Xinu operating system to both platforms, and continue to expand the capabilities of the software. Specifically, we are exploring external interfaces (such as GPIO capabilities), memory management (including page table management), and networked facilities, such as a remote file system. We are also considering multicore systems.

There is a paradigm shift in database transaction processing (OLTP) from the perspective of both hardware and software. The hardware trends are a cheaper and larger main memory and a larger number of cores per processor. These technological trends are paving the way for OLTP databases to become entirely memory-resident (substantially lower latency) and to potentially support more concurrent environment (substantially higher throughput). The software trends are to exploit latch-free and low-maintenance data structures to allow concurrent readers and writers of data to proceed together by minimizing the contention among them. However, the research in the database community has been dominated by disk-based and block-accessible data structures that are designed to reduce the number of random physical addresses and have paid a little attention to latch-free (or lock-free), byte-addressable, memory-resident data structures. Therefore,the core idea of this summer project is to develop a highly concurrent, low-maintenance, in-memory data structure to support a wide spectrum of queries including point and range queries than can benefit both OLTP and OLAP workloads.

Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). Moreover, what users are doing now may be unrelated to what they will be doing in an hour from now. Mindful of these challenges this project seeks to design machine learning methods that can learn these hidden networks, analyze their structure, and predict user trajectories while coping with the complex challenges of learning personalized predictive models of non-stationary, transient, and inhomogeneous behavior of millions of users.

Enabling the Internet of Value: Moving money the same way as the information moves today (Mentor Aniket Kate)

Cryptocurrencies such as Bitcoin and Ethereum have emerged as a paradigm shift for the way payment systems work today. Cryptocurrencies rely on the blockchain, a technology that has been proven useful in a vast number of applications other than monetary transactions. Many companies today are tailoring the blockchain technology to their business logic and successfully developing applications for credit settlement networks, supply chain, IoT and beyond. However, these separate efforts are leading to incompatible individual systems. This contrasts with our highly interconnected world and it is inevitable to see that soon these blockchains will need to operate with each other, effectively forming a network of blockchains where transactions can flow through a sequence of blockchains, similar how the network of networks (i.e., the Internet) works today.

In this project, we plan to explore how to connect the different blockchains in a secure manner, a task that requires to research into many interesting and challenging problems. For instance, in such a scenario it is crucial to devise an scalable mechanism to find routes between two users enrolled in different blockchains. Moreover, we require an accountability mechanism for provable guarantees of delivery of funds in a transaction. In a bit more detail, we want a proof that convinces the sender of that funds are delivered to the receiver, while receiver cannot falsely claim that such delivery has not occurred. In summary, in this project, we will design and evaluate the tools required to move money the same way as the information moves today, therefore enabling the Internet of Value.

Today most people are susceptible of oversharing their personal information on social networks such as Facebook, Twitter, Instagram, and Snapchat. This oversharing raises numerous privacy concerns for these users. Therefore, most online social platforms offer mechanisms allowing users to withdraw their information. In fact a significant fraction of users exercise this right to be forgotten. However, the existing withdrawal mechanisms leave users more vulnerable to privacy attacks: due to the now popular "Streisand effect", a phenomenon whereby an attempt to hide some information has the unintended consequence of bringing particular attention of public to it, including curious friends, cyberstalking and even blackmailers, who can now focus only on the withdrawals to see which among those were sensitive.In the past, our team has started to tackle this problem by introducing inactivity based withdrawals and even a more disruptive solution of interrupting the availability of non-withdrawn post to provide differential-privacy inspired privacy guarantees. Due to the importance of this problem and lack of much prior work in this field, there are still lots of work that needs to be done towards providing privacy for content deletions. For example, We have shown the privacy guarantees for social platforms such as Twitter, however, there is also a need for such systems in archival platforms e.g. archive.org. Another important study that we look forward to perform is a user behavioral study for the introduced systems to observe the effectiveness and practicality of these privacy systems in the eyes of users. In this project, we take the next few step towards making social network users forget things that they have forgotten.

The human brain mapping project seeks to map neurons and synapses in the brain to understand how they are involved in the functions of the brain. There are a 100 billion neurons and 100 trillion synapses in the human brain, and current technology cannot map them individually. Nor can current algorithms model the neurons and the synapses at this scale. However, coarser models that can measure blood flow in the various regions of the brain can identify regions of the brain (voxels) that are activated under particular conditions. We are interested in taking two or more brain graphs (of the same individual under different conditions, or different individuals) created from functional MRI data, and align the subnetworks in them to each other. By aligning the subnetworks, we should be able to confidently identify regions of the brain that are responsible for particular functions. This problem involves a sophisticated mathematical formulation (integer linear programming) with matching algorithms in graphs. In this project, we will study an algorithm that we have proposed in earlier work (Supercomputing 2012) that is capable of aligning networks with hundreds of thousands of nodes and edges in a few seconds. We will implement this algorithm on a multithreaded desktop computer, create efficient implementations, and evaluate it on several brain graphs.

Abstract meaning representation (AMR) is a challenging problem in natural language processing, in which one aims to transform a given text into a rich graphical representation of its meaning. A rooted, directed, acyclic graph is a semantic formalism where nodes represent concepts, and labeled directed edges represent the relationships between them. When learning in a supervised setting, the learner receives a set of sentence/graph pairs for training. A successful learner then correctly predicts graphs for previously unseen sentences. Our project aims to analyze long texts, for which previously developed heuristics do not work well in practice.

Students working in this project will get knowledge of the relevant machine learning and natural language processing techniques used for parsing AMR sentences. Possible research activities include the design and evaluation of proper feature mappings, which are very important for guiding the learning process. Another activity would be the experimental comparison of the performance of different competing methods on several publicly available datasets.

Consider business analytics, if we are able to understand human locations, postures and gestures in a shopping mall, we will be able to understand their shopping behavior. Interestingly, this is already happening extensively in the web - our clicking patterns, mouse movements, etc. are all driving a billion-dollar business, called web analytics. Our footsteps through indoor environments are indeed like our clicking patterns. When we look at a cereal box in the grocery store, it is indeed like right clicking on an online item. Web and "physical world analytics" are so similar, and yet physical analytics is just not present today, simply because we don't have the ability to understand locations, postures, and gestures. If we enable indoor localization, posture and gesture inferences, we can enable physical business analytics. In this summer project, we will design and implement physical business analytics systems.